stable-diffusion-2-1-unclip
Maintainer: stabilityai
252
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The stable-diffusion-2-1-unclip model is a finetuned version of the Stable Diffusion 2.1 model developed by Stability AI. It can accept noisy CLIP image embeddings in addition to text prompts, allowing for image variations and chaining with CLIP priors. The amount of noise added to the image embedding can be controlled via the noise_level parameter.
Model inputs and outputs
The stable-diffusion-2-1-unclip model is a diffusion-based text-to-image generation model. It takes a text prompt as input and generates a corresponding image as output.
Inputs
- Text prompt: A text description of the desired image.
- Noise level: A value between 0 and 1000 controlling the amount of noise added to the CLIP image embedding.
Outputs
- Generated image: An image corresponding to the provided text prompt.
Capabilities
The stable-diffusion-2-1-unclip model can generate a wide variety of images based on text prompts, including realistic scenes, abstract art, and imaginative compositions. It is particularly capable of generating high-quality images with a high level of detail.
What can I use it for?
The stable-diffusion-2-1-unclip model is intended for research purposes, such as:
- Probing the limitations and biases of generative models.
- Generating artworks and using the model in design and other creative processes.
- Developing educational or creative tools.
- Researching generative models.
However, the model should not be used to intentionally create or disseminate images that could be harmful or offensive to people.
Things to try
One interesting aspect of the stable-diffusion-2-1-unclip model is its ability to accept noisy CLIP image embeddings as input, in addition to text prompts. This allows you to generate image variations and chain the model with CLIP priors. Try experimenting with different noise levels to see how it affects the generated images.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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